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Size and shape analysis of error-prone shape data

Du, J.; Dryden, Ian L.; Huang, X.


J. Du

Professor of Statistics

X. Huang


We consider the problem of comparing sizes and shapes of objects when landmark data are prone to measurement error. We show that naive implementation of ordinary Procrustes analysis that ignores measurement error can compromise inference. To account for measurement error, we propose the conditional score method for matching configurations, which guarantees consistent inference under mild model assumptions. The effects of measurement error on inference from naive Procrustes analysis and the performance of the proposed method are illustrated via simulation and application in three real data examples. Supplementary materials for this article are available online.


Du, J., Dryden, I. L., & Huang, X. (2015). Size and shape analysis of error-prone shape data. Journal of the American Statistical Association, 110(509),

Journal Article Type Article
Acceptance Date Mar 17, 2014
Online Publication Date Apr 18, 2014
Publication Date Mar 1, 2015
Deposit Date Mar 7, 2017
Publicly Available Date Mar 7, 2017
Journal Journal of the American Statistical Association
Print ISSN 0162-1459
Electronic ISSN 1537-274X
Publisher Taylor & Francis Open
Peer Reviewed Peer Reviewed
Volume 110
Issue 509
Keywords Complex normal; Configuration; Landmark; Ordinary Procrustes analysis; Quaternion
Public URL
Publisher URL


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